The big data challenge is about making sense of large
collections of complex and dynamic information, and using them as
assets for business intelligence and other forms of analytics-based insight.
Example applications include smart advertising and marketing based on user-action logs,
intelligent control of complex logistics for traffic and energy, or
identifying trends and opinions in social media in the Internet.
Our group is exploring ways to efficiently and effectively mine
emerging topics in
streams of user-generated Web 2.0 contents
and other kinds of interesting patterns in highly dynamic data
generated by sensors or in business settings.

News:

Our group is in transition phase to TU Kaiserslautern, where Sebastian accepted a professorship, starting August 2014.

Our paper on Tracking Set Correlations at Large Scale got accepted for publication at SIGMOD 2014.

New DFG Project: The German Research Foundation (DFG) has approved the project "Pantheon: Efficiently Creating and Maintaining Semantically Meaningful Entity Rankings at Large Scale". More details soon.

Best Student Paper Award for the paper:
Scalable, Continuous Tracking of Tag Co-Occurrences between Short Sets using (Almost) Disjoint Tag Partitions. At DBSocial@SIGMOD, New York, NY, USA.

Two new papers at dbsocial@SIGMOD, on event detection in top-k rankings and on scalable, continuous computation
of set correlations.

Busy Beaver Award for the best seminar in computer science for our seminar on
Non-Traditional Data Management.

Upcoming summer term: Lecture on Distributed Data Management at Kaiserslautern University of Technology. See here